Diabetes care management system

ABSTRACT

A diabetes care management system for managing blood glucose levels associated with diabetes, comprising a computing device and an insulin delivery device. The computing device generally includes (i) a memory comprising one or more optimal blood glucose values, one or more insulin dose values of a patient, one or more measured blood glucose values, and one or more scaling factors for weighting the impact on a future blood glucose value and that are customizable to an individual patient to predict the effect on the blood glucose of insulin dose actions performed by the individual patient, (ii) a microprocessor, in communication with the memory, programmed to calculate a further value, the further value being based on the insulin dose values, the optimal blood glucose values, and the scaling factors, (iii) a display configured to display information according to the further value, and (iv) a housing, wherein the memory and the microprocessor are housed within the housing, thereby providing a hand-held, readily transportable computing device. The insulin delivery device may deliver insulin in response to information associated with the further value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Ser. No. 11/656,168, filed Jan.22, 2007, which is a continuation of application Ser. No. 09/810,865,filed Mar. 16, 2001, now U.S. Pat. No. 7,167,818, which is acontinuation of Ser. No. 09/399,122, filed Sep. 20, 1999, now U.S. Pat.No. 6,233,539, which is a continuation of Ser. No. 08/781,278 filed Jan.10, 1997, now U.S. Pat. No. 5,956,501, each of which are hereinincorporated by reference.

BACKGROUND

1. Field of the Invention

The present invention relates generally to disease simulation systems,and in particular to a system and method for simulating a diseasecontrol parameter and for predicting the effect of patient self-careactions on the disease control parameter.

2. Description of Prior Art

Managing a chronic disease or ongoing health condition requires themonitoring and controlling of a physical or mental parameter of thedisease. Examples of these disease control parameters include bloodglucose in diabetes, respiratory flow in asthma, blood pressure inhypertension, cholesterol in cardiovascular disease, weight in eatingdisorders, T-cell or viral count in HIV, and frequency or timing ofepisodes in mental health disorders. Because of the continuous nature ofthese diseases, their corresponding control parameters must be monitoredand controlled on a regular basis by the patients themselves outside ofa medical clinic.

Typically, the patients monitor and control these parameters inclinician assisted self-care or outpatient treatment programs. In thesetreatment programs, patients are responsible for performing self-careactions which impact the control parameter. Patients are alsoresponsible for measuring the control parameter to determine the successof the self-care actions and the need for further adjustments. Thesuccessful implementation of such a treatment program requires a highdegree of motivation, training, and understanding on the part of thepatients to select and perform the appropriate self-care actions.

One method of training patients involves demonstrating the effect ofvarious self-care actions on the disease control parameter throughcomputerized simulations. Several computer simulation programs have beendeveloped specifically for diabetes patients. Examples of suchsimulation programs include BG Pilot™ commercially available from RayaSystems, Inc. of 2570 El Camino Real, Suite 520, Mountain View, Calif.94040 and AIDA freely available on the World Wide Web at the Diabetes UKwebsite http://www.pcug.co.uk/diabetes/aida.htm.

Both BG Pilot™ and AIDA use mathematical compartmental models ofmetabolism to attempt to mimic various processes of a patient'sphysiology. For example, insulin absorption through a patient's fattytissue into the patient's blood is represented as a flow through severalcompartments with each compartment having a different flow constant.Food absorption from mouth to stomach and gut is modeled in a similarmanner. Each mathematical compartmental model uses partial differentialequations and calculus to simulate a physiological process.

This compartmental modeling approach to disease simulation has severaldisadvantages. First, understanding the compartmental models requiresadvanced mathematical knowledge of partial differential equations andcalculus which is far beyond the comprehension level of a typicalpatient. Consequently, each model is an unfathomable “black box” to thepatient who must nevertheless trust the model and rely upon it to learncritical health issues.

A second disadvantage of the compartmental modeling approach is that anew model is needed for each new disease to be simulated. Many diseasesinvolve physiological processes for which accurate models have not beendeveloped. Consequently, the mathematical modeling approach used in BGPilot™ and AIDA is not sufficiently general to extend simulations todiseases other than diabetes.

A further disadvantage of the modeling approach used in BG Pilot™ andAIDA is that the mathematical models are not easily customized to anindividual patient. As a result, BG Pilot™ and AIDA are limited tosimulating the effect of changes in insulin and diet on the bloodglucose profile of a typical patient. Neither of these simulationprograms may be customized to predict the effect of changes in insulinand diet on the blood glucose profile of an individual patient.

OBJECTS AND ADVANTAGES OF THE INVENTION

In view of the above, it is an object of the present invention toprovide a disease simulation system which is sufficiently accurate toteach a patient appropriate self-care actions and sufficiently simple tobe understood by the average patient. It is another object of theinvention to provide a disease simulation system which may be used tosimulate many different types of diseases. A further object of theinvention is to provide a disease simulation system which may be easilycustomized to an individual patient.

These and other objects and advantages will become more apparent afterconsideration of the ensuing description and the accompanying drawings.

SUMMARY OF THE INVENTION

The invention presents a system and method for simulating a diseasecontrol parameter and for predicting the effect of patient self-careactions on the disease control parameter. According to the method, afuture disease control parameter value X(t_(j)) at time t_(j) isdetermined from a prior disease control parameter value X(t_(i)) at timet_(i) based on an optimal control parameter value R(t_(j)) at timet_(j), the difference between the prior disease control parameter valueX(t_(i)) and an optimal control parameter value R(t_(i)) at time t_(i),and a set of differentials between patient self-care parameters havingpatient self-care values S_(M)(t_(i)) at time t_(i) and optimalself-care parameters having optimal self-care values O_(M)(t_(i)) attime t_(i). In the preferred embodiment, the differentials aremultiplied by corresponding scaling factors K_(M) and the future diseasecontrol parameter value X(t_(j)) is calculated according to theequation:${X( t_{j} )} = {{R( t_{j} )} + ( {{X( t_{i} )} - {R( t_{i} )}} ) + {\sum\limits_{M}\quad{K_{M}( {{S_{M}( t_{i} )} - {O_{M}( t_{i} )}} )}}}$

A preferred system for implementing the method includes an input devicefor entering the patient self-care values S_(M)(t_(i)). The system alsoincludes a memory for storing the optimal control parameter valuesR(t_(i)) and R(t_(j)), the prior disease control parameter valueX(t_(i)), the optimal self-care values O_(M)(t_(i)), and the scalingfactors K_(M). A processor in communication with the input device andmemory calculates the future disease control parameter value X(t_(j)). Adisplay is connected to the processor to display the future diseasecontrol parameter value X(t_(j)) to a patient.

In the preferred embodiment, the system further includes a recordingdevice in communication with the processor for recording an actualcontrol parameter value A(t_(i)) at time t_(i), an actual controlparameter value A(t_(j)) at time t_(j), and actual self-care parametershaving actual self-care values C_(M)(t_(i)) at time t_(i). The processoradjusts the scaling factors K_(M) based on the difference between theactual control parameter value A(t_(j)) and the optimal controlparameter value R(t_(j)), the difference between the actual controlparameter value A(t_(i)) and the optimal control parameter valueR(t_(i)), and the difference between the actual self-care valuesC_(M)(t_(i)) and the optimal self-care values O_(M)(t_(i)). Thus, thescaling factors K_(M) are customized to an individual patient to predictthe effect on the disease control parameter of self-care actionsperformed by the individual patient.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a simulation system according to theinvention.

FIG. 2 is a sample physiological parameter entry screen according to theinvention.

FIG. 3 is a sample self-care parameter entry screen according to theinvention.

FIG. 4 is a table of values according to the invention.

FIG. 5 is a sample graph of disease control parameter values createdfrom the table of FIG. 4.

FIG. 6 is another table of values according to the invention.

FIG. 7 is a sample graph of disease control parameter values createdfrom the table of FIG. 6.

FIG. 8 is another table of values according to the invention.

FIG. 9 is a sample graph of disease control parameter values createdfrom the table of FIG. 8.

FIG. 10 is a schematic illustration of the entry of actual parametervalues in a recording device of the system of FIG. 1.

FIG. 11 is a schematic diagram of another simulation system according tothe invention.

FIG. 12 is a schematic block diagram illustrating the components of thesystem of FIG. 11.

FIG. 13 is a flow chart illustrating steps included in a method of theinvention.

FIG. 14 is a flow chart illustrating steps included in another method ofthe invention.

DESCRIPTION

The present invention is a system and method for simulating a diseasecontrol parameter and for predicting an effect of patient self-careactions on the disease control parameter. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the present invention. However, it will beapparent to one of ordinary skill in the art that these specific detailsneed not be used to practice the invention. In other instances, wellknown structures, interfaces, and processes are not shown in detail toavoid unnecessarily obscuring the present invention.

FIGS. 1-10 illustrate a preferred embodiment of a simulation systemaccording to the invention. The following table illustrates arepresentative sampling of the types of diseases, patient self-careparameters, and disease control parameters which may be simulated usingthe system and method of the invention. Disease Self-Care ParametersControl Parameter Diabetes insulin, diet, exercise blood glucose levelAsthma allergens, exercise, inhaled bronchial peak flow rate dilators,anti-inflammatory medications Obesity diet, exercise, metabolismaltering weight medications Hypertension diet, exercise, stressreduction, blood blood pressure pressure medications Coronary ArteryDisease diet, exercise, stress reduction, lipid cholesterol loweringmedications Panic Disorder stress reduction, anti-depressant number ofepisodes medications Nicotine Addiction cigarettes smoked, copingbehaviors urges to smoke

The above table is not intended as an exhaustive list, but merely as arepresentative sampling of the types of diseases and disease controlparameters which may be simulated. For simplicity, the preferredembodiment is described with reference to a single disease, diabetes,having a single disease control parameter, a blood glucose level.However, it is to be understood that the system and method of theinvention are sufficiently flexible to simulate any disease which has ameasurable control parameter and which requires patient self-careactions.

Referring to FIG. 1, a simulation system generally indicated at 10includes a server 12 having a processor and memory for executing asimulation program which will be described in detail below. Server 12 isin communication with a healthcare provider computer 14 through anetwork link 48.

Healthcare provider computer 14 is preferably a personal computerlocated at a healthcare provider site, such as a doctor's office.

Server 12 is also in communication with a patient multi-media processor24 through a network link 50. Patient multi-media processor 24 islocated at a patient site, typically the patient's home. In thepreferred embodiment, server 12 is a world wide web server, multi-mediaprocessor 24 is a web TV processor for accessing the simulation programon server 12, and links 48 and 50 are Internet links. Specifictechniques for establishing client/server computer systems in thismanner are well known in the art.

Healthcare provider computer 14 includes a processor and memory, astandard display 16, and a keyboard 20. Computer 14 further includes acard slot 18 for receiving a data storage card, such as a smart card 22.Computer 14 is designed to read data from card 22 and write data to card22. Patient multi-media processor 24 includes a corresponding card slot26 for receiving card 22. Processor 24 is designed to read data fromcard 22 and write data to card 22. Thus, healthcare provider computer 14communicates with patient multi-media processor 24 via smart card 22.Such smart card data communication systems are also well known in theart.

Multi-media processor 24 is connected to a display unit 28, such as atelevision, by a standard connection cord 32. Display unit 28 has ascreen 30 for displaying simulations to the patient. An input device 34,preferably a conventional hand-held remote control unit or keyboard, isin signal communication with processor 24. Device 34 has buttons or keys36 for entering data in processor 24.

System 10 also includes an electronic recording device 38 for recordingactual control parameter values and patient self-care data indicatingactual self-care actions performed by the patient. Recording device 38includes a measuring device 40 for producing measurements of the diseasecontrol parameter, a keypad 44 for entering the self-care data, and adisplay 42 for displaying the control parameter values and self-caredata to the patient.

Recording device 38 is preferably portable so that the patient may carrydevice 38 and enter the self-care data at regular monitoring intervals.Device 38 is further connectable to healthcare provider computer 14 viaa standard connection cord 46 so that the control parameter values andpatient self-care data may be uploaded from device 38 to computer 14.Such recording devices for producing measurements of a disease controlparameter and for recording self-care data are well known in the art.For example, U.S. Pat. No. 5,019,974 issued to Beckers on May 28, 1991discloses such a recording device.

In the example of the preferred embodiment, the disease controlparameter is the patient's blood glucose level and recording device 38is a blood glucose meter, as shown in FIG. 10. In this embodiment,measuring device 40 is a blood glucose test strip designed to test bloodreceived from a patient's finger 54. Device 38 is also designed torecord values of the patient's diet, medications, and exercise durationsentered by the patient through keypad 44. Of course, in alternativeembodiments, the recording device may be a peak flow meter for recordinga peak flow rate, a cholesterol meter for recording a cholesterol level,etc.

The simulation system of the present invention includes a simulationprogram which uses a mathematical model to calculate disease controlparameter values. The following variables used in the mathematical modelare defined as follows:

N=Normal time interval in which patient self-care actions are employedto make a measurable difference in the disease control parameter or anatural rhythm occurs in the disease control parameter. For diabetes andasthma, time interval N is preferably twenty-four hours. For obesity orcoronary artery disease, time interval N is typically three to sevendays.

t₁, t₂, . . . t_(i), t_(j) . . . t_(N)=Time points at which the diseasecontrol parameter is measured by a patient. For a daily rhythm controlparameter such as a blood glucose level, the time points are preferablybefore and after meals. For weight or cholesterol control parameters,the time points are preferably once a day or once every second day.

X(t)=Simulated disease control parameter value at time t determined bythe simulation program.

R(t)=Optimal control parameter value at time t expected as a normalrhythm value of the disease control parameter at time t if the patientperforms optimal self-care actions in perfect compliance from time t_(j)to the time point immediately preceding time t.

A(t)=actual control parameter value at time t measured by the patient.

O_(M)(t_(i))=Optimal self-care parameter values O₁(t_(i)), O₂(t_(i)), .. . O_(m)(t_(i)) at time t_(i) expected to produce optimal controlparameter value R(t_(j)) at time t_(j). For example, a diabetespatient's optimal self-care parameter values include a prescribed doseof insulin, a prescribed intake of carbohydrates, and a prescribedexercise duration.

S_(M)(t_(i))=Patient self-care parameter values S₁(t_(i)), S₂(t_(i)), .. . S_(m)(t_(i)) at time t_(i) entered in the simulation system by thepatient to simulate self-care actions.

C_(M)(t_(i))=Actual self-care parameter values C₁(t_(i)), C₂(t_(i)), . .. C_(m)(t_(i)) at time t_(i) indicating actual self-care actionsperformed by the patient at time t_(i).

K_(m)=Corresponding scaling factors K₁(t_(i)), K₂(t_(i)), . . . K_(m)for weighting the impact on a future disease control parameter valueX(t_(j)) at time t_(j) which results from differentials between patientself-care values S_(M)(t_(i)) and corresponding optimal self-care valuesO_(M)(t_(i)).

With these definitions, future disease control parameter value X(t_(j))is calculated according to the equation:${X( t_{j} )} = {{R( t_{j} )} + ( {{X( t_{i} )} - {R( t_{i} )}} ) + {\sum\limits_{M}\quad{K_{M}( {{S_{M}( t_{i} )} - {O_{M}( t_{i} )}} )}}}$

Future disease control parameter value X(t_(j)) at time t_(j) isdetermined from a prior disease control parameter value X(t_(i)) at timet_(i) based on an optimal control parameter value R(t_(j)) at timet_(j), the difference between prior disease control parameter valueX(t_(i)) and an optimal control parameter value R(t_(i)) at time t_(i),and a set of differentials between patient self-care values S_(M)(t_(i))and optimal self-care values O_(M)(t_(i)). The differentials aremultiplied by corresponding scaling factors K_(M).

Thus, as patient self-care parameter values S_(M)(t_(i)) deviate fromoptimal self-care parameter values O_(M)(t_(i)), future disease controlparameter value X(t_(j)) deviates from optimal control parameter valueR(t_(j)) by an amount proportional to scaling factors K_(M). Thismathematical model follows the patient's own intuition and understandingthat if the patient performs optimal self-care actions in perfectcompliance, the patient will achieve the optimal control parameter valueat the next measurement time. However, if the patient deviates from theoptimal self-care actions, the disease control parameter value willdeviate from the optimal value at the next measurement time.

The simulation program is also designed to generate an entry screen forentry of the patient self-care parameter values. FIG. 3 shows a samplepatient self-care parameters entry screen 52 as it appears on displayunit 28. The patient self-care parameters include a food exchangeparameter expressed in grams of carbohydrates consumed, an insulin doseparameter expressed in units of insulin injected, and an exerciseduration parameter expressed in fifteen minute units of exerciseperformed.

These self-care parameters are illustrative of the preferred embodimentand are not intended to limit the scope of the invention. Many differentself-care parameters may be used in alternative embodiments. Screen 52contains data fields 53 for entering a food exchange parameter valueS₁(t), an insulin dose parameter value S₂(t), and an exercise durationparameter value S₃(t). Each data field 53 has a corresponding time field51 for entering a time point corresponding to the patient self-careparameter value. Screen 52 also includes an OK button 55 and a cancelbutton 57 for confirming and canceling, respectively, the values enteredin screen 52.

FIG. 4 shows a sample table of values 56 created by the simulationprogram using the data entered by the patient through the self-careparameters entry screen. Table 56 includes a column of simulated diseasecontrol parameter values calculated by the simulation program, as willbe explained in the operation section below. The simulation program isfurther designed to generate graphs of simulated disease controlparameter values. FIG. 5 illustrates a sample graph 58 generated fromtable 56 as it appears on screen 30 of the display unit. Specifictechniques for writing a simulation program to produce such a graph arewell known in the art.

In the preferred embodiment, healthcare provider computer 14 isprogrammed to determine scaling factors K_(M) from values ofphysiological parameters of the patient. FIG. 2 shows a samplephysiological parameter entry screen 41 as it appears on the healthcareprovider computer. The physiological parameters of the patient include abody mass, a metabolism rate, a fitness level, and hepatic andperipheral insulin sensitivities. These physiological parameters areillustrative of the preferred embodiment and are not intended to limitthe scope of the invention. Many different physiological parameters maybe used in alternative embodiments. Screen 41 includes data fields 43for entering physiological parameter values, an OK button 45 forconfirming the values, and a cancel button 47 for canceling the values.

Healthcare provider computer 14 stores indexes for determining thescaling factors from the physiological parameters entered. For example,FIG. 4 shows an insulin sensitivity scaling factor K₂ corresponding toinsulin dose parameter value S₂(t). Computer 14 is programmed todetermine from a stored insulin index a value of scaling factor K₂ basedon the entered values of the patient's body mass and insulinsensitivities. In this example, computer 14 determines a value of −40for scaling factor K₂, indicating that for this patient, one unit ofinsulin is expected to lower the patient's blood glucose level by 40mg/dL. Computer 14 is programmed to determine the remaining scalingfactors in a similar manner. The specific indexes required to determinethe scaling factors from values of a patient's physiological parametersare well known in the art.

In the preferred embodiment, healthcare provider computer 14 is alsoprogrammed to adjust scaling factors K_(M) based on the differencebetween an actual control parameter value A(t_(j)) measured at timet_(j) and optimal control parameter value R(t_(j)), the differencebetween an actual control parameter value A(t_(i)) measured at timet_(i) and optimal control parameter value R(t_(i)), and the differencebetween actual self-care values C_(M)(t_(i)) performed by the patient attime t_(i) and optimal self-care values O_(M)(t_(i)).

Scaling factors K_(M) are adjusted to fit the mathematical modelpresented above, preferably using a least squares, chi-squares, orsimilar regressive fitting technique. Specific techniques for adjustingcoefficients in a mathematical model are well known in the art. Forexample, a discussion of these techniques is found in “Numerical Recipesin C: The Art of Scientific Computing”, Cambridge University Press,1988.

The operation of the preferred embodiment is illustrated in FIG. 13.FIG. 13 is a flow chart illustrating a preferred method of using system10 to simulate the disease control parameter. In step 200, optimalself-care values and optimal control parameter values for each timepoint are determined for the patient, preferably by the patient'shealthcare provider. The optimal self-care values and optimal controlparameter values are then entered and stored in provider computer 14.

In the preferred embodiment, the optimal self-care values include anoptimal food exchange parameter value O₁(t) expressed in grams ofcarbohydrates, an optimal insulin dose parameter value O₂(t) expressedin units of insulin, and an optimal exercise duration parameter valueO₃(t) expressed in fifteen minute units of exercise. Specific techniquesfor prescribing optimal self-care values and optimal control parametervalues for a patient are well known in the medical field.

In step 202, the healthcare provider determines the physiologicalparameter values of the patient and enters the physiological parametervalues in computer 14 through entry screen 41. As shown in FIG. 2, thephysiological parameter values include a body mass, a metabolism rate, afitness level, and hepatic and peripheral insulin sensitivities.Specific techniques for testing a patient to determine thesephysiological parameter values are also well known in the medical field.

Following entry of the physiological parameter values, computer 14determines scaling factors K_(M) from the stored indexes, step 204. Forexample, FIG. 4 shows a food exchange scaling factor K₁ corresponding tofood exchange parameter value S₁(t), an insulin sensitivity scalingfactor K₂ corresponding to insulin dose parameter value S₂(t), and anexercise duration scaling factor K₃ corresponding to exercise durationparameter value S₃(t).

In this example, computer 14 determines a value of 4 for scaling factorK₁, a value of −40 for scaling factor K₂, and a value of −5 for scalingfactor K₃. These values indicate that one gram of carbohydrate isexpected to raise the patient's blood glucose level by 4 mg/dL, one unitof insulin is expected to lower the patient's blood glucose level by 40mg/dL, and fifteen minutes of exercise is expected to lower thepatient's blood glucose level by 5 mg/dL. Of course, these values arejust examples of possible scaling factors for one particular patient.The values of the scaling factors vary between patients in dependenceupon the physiological parameter values determined for the patient.

The determined optimal self-care values, optimal control parametervalues, and scaling factors are then stored on smart card 22, step 206.Typically, the values are stored on smart card 22 during a patient visitto the healthcare provider. The patient then takes home smart card 22and inserts smart card 22 in patient multi-media processor 24, step 208.Next, the patient accesses the simulation program on server 12 throughmulti-media processor 24, step 210.

The simulation program generates self-care parameters entry screen 52,which is displayed to the patient on screen 30 of display unit 28. Instep 212, the patient enters patient self-care values S_(M)(t) andcorresponding time points in data fields 53 and 51, respectively, usinginput device 34. The optimal self-care values, optimal control parametervalues, scaling factors, and patient self-care values are transmittedfrom multi-media processor 24 to server 12 through link 50. In step 214,the simulation program calculates simulated disease control parametervalues at each time point according to the equation:${X( t_{j} )} = {{R( t_{j} )} + ( {{X( t_{i} )} - {R( t_{i} )}} ) + {\sum\limits_{M}{M\quad( {{S_{M}( t_{i} )} - {O_{M}( t_{i} )}} )}}}$

Thus, each future disease control parameter value X(t_(j)) is calculatedfrom optimal control parameter value R(t_(i)), the difference betweenprior disease control parameter value X(t_(i)) and optimal controlparameter value R(t_(i)), and the set of differentials between patientself-care values S_(M)(t_(i)) and optimal self-care values O_(M)(t_(i)).The differentials are multiplied by corresponding scaling factors K_(M).In the preferred embodiment, first simulated disease control parametervalue X(t₁) at time t_(j) is set equal to first optimal controlparameter value R(t₁) at time t₁. In an alternative embodiment, firstsimulated disease control parameter value X(t₁) is determined from thelast disease control parameter value calculated in a prior simulation.

FIGS. 4-5 illustrate a first example of simulated disease controlparameter values calculated by the simulation program. Referring to FIG.4, the simulation program creates table of values 56 having a timecolumn, an optimal control parameter value column, a simulated controlparameter value column, three self-care value differential columnsindicating differentials between patient self-care parameter values andoptimal self-care parameter values, and three corresponding scalingfactor columns for weighting the corresponding self-care valuedifferentials.

Table 56 illustrates the simplest simulation, in which the patientfollows the optimal self-care actions in perfect compliance at each timepoint. In this simulation, each patient self-care parameter value equalsits corresponding optimal self-care parameter value, so that thesimulated disease control parameter value at each time point is simplyequal to the optimal control parameter value at each time point.Referring to FIG. 5, the simulation program generates graph 58 of thesimulated disease control parameter values.

Graph 58 is displayed to the patient on screen 30 of display unit 28,step 216.

FIGS. 6-7 illustrate a second example of simulated disease controlparameter values calculated by the simulation program. FIG. 6 shows atable of values 59 having identical structure to table 56. Table 59illustrates a simulation in which the patient consumes 10 extra grams ofcarbohydrates at 8:00 and exercises for 60 extra minutes at 15:00. Inthis simulation, the differential S₁(t)-O₁(t) is equal to 10 at 8:00 dueto the 10 extra grams of carbohydrates consumed by the patient. Becausescaling factor K₁ equals 4, the simulation program calculates simulateddisease control parameter value X(t₂) at time point 10:00 as 40 mg/dLhigher than optimal control parameter value R(t₂) at 10:00.

Similarly, the differential S₃(t)-O₃(t) is equal to 4 at time point15:00 due to the 60 extra minutes of exercise performed by the patient.With simulated disease control parameter value X(t₄) exceeding optimalcontrol parameter value R(t₄) by 40 mg/dL at 15:00 and with scalingfactor K₃ equal to −5, the simulation program calculates simulateddisease control parameter value X(t₅) at time point 18:00 as 20 mg/dLhigher than optimal control parameter value R(t₅). FIG. 7 shows a graph60 of the simulated-disease control parameter values determined in table59. Graph 60 is displayed to the patient on screen 30 of the displayunit.

FIGS. 8-9 illustrate a third example of simulated disease controlparameter values calculated by the simulation program. Referring to FIG.8, a table of values 61 illustrates a simulation in which the patientconsumes 10 extra grams of carbohydrates at 8:00, injects 1 extra unitof insulin at 10:00, and exercises for 60 extra minutes at 15:00. Thedifferential S₂(t)-O₂(t) is equal to 1 at 10:00 due to the 1 extra unitof insulin injected by the patient. With simulated disease controlparameter value X(t₂) exceeding optimal control parameter value R(t₂) by40 mg/dL at 10:00, and with scaling factor K₂ equal to −40, thesimulation program calculates simulated disease control parameter valueX(t₃) at time point 12:00 as equal to optimal control parameter valueR(t₃). FIG. 8 shows a graph 62 of the simulated disease controlparameter values determined in table 61.

In addition to performing simulations with the simulation program, thepatient records actual control parameter values and actual self-carevalues indicating actual self-care actions performed by the patient ateach time point, step 218. These values are preferably recorded inrecording device 38. Upon the patient's next visit to the healthcareprovider, the actual control parameter values and actual self-carevalues are uploaded to provider computer 14, step 220. Those skilled inthe art will appreciate that recording device 38 may also be networkedto provider computer 14 through a modem and telephone lines or similarnetwork connection. In this alternative embodiment, the actual controlparameter values and actual self-care values are transmitted directlyfrom the patient's home to provider computer 14.

In step 222, provider computer 14 adjusts scaling factors K_(M) based onthe difference between the actual control parameter values and theoptimal control parameter values at each time point and the differencebetween the actual self-care values and the optimal self-care values ateach time point. Scaling factors K_(M) are adjusted to fit them to theactual patient data recorded. In this manner, the scaling factors arecustomized to the individual patient to enable the patient to runcustomized simulations. The new values of the scaling factors are storedon smart card 22 which the patient takes home and inserts in processor24 to run new simulations.

FIGS. 11-12 illustrate a second embodiment of the invention. The secondembodiment differs from the preferred embodiment in that the componentsof the simulation system are contained in a single stand-alone computingdevice 64. The second embodiment also differs from the preferredembodiment in that the system predicts each future disease controlparameter value from an actual measured disease control parameter valuerather than from a prior simulated disease control parameter value.

Referring to FIG. 11, computing device 64 includes a housing 66 forholding the components of device 64. Housing 66 is sufficiently compactto enable device 64 to be hand-held and carried by a patient. Device 64also includes measuring device 40 for producing measurements of actualcontrol parameters values and a display 70 for displaying data to thepatient. Device 64 further includes a keypad 68 for entering in device64 the optimal control parameter values, the optimal self-care values,the patient self-care parameter values, the actual self-care parametervalues, and the patient's physiological parameter values.

FIG. 12 shows a schematic block diagram of the components of device 64and their interconnections. Device 64 has a microprocessor 72 and amemory 74 operably connected to microprocessor 72. Measuring device 40and display 70 are also connected to microprocessor 72. Keypad 68 isconnected to microprocessor 72 through a standard keypad decoder 78.Microprocessor 72 is connected to an input/output port 76 for enteringin device 64 a simulation program to be executed by microprocessor 72which will be explained in detail below.

Memory 74 stores the optimal control parameter values, the optimalself-care values, the patient self-care parameter values, the actualself-care parameter values C_(M)(t), the scaling factors, and thepatient's physiological parameter values. Memory 74 also stores thesimulation program to be executed by microprocessor 72 and the indexesfor calculating the scaling factors from the patient's physiologicalparameter values.

In the second embodiment, microprocessor 72 is programmed to perform thefunctions performed by the healthcare provider computer of the preferredembodiment. The functions include determining scaling factors K_(M) fromthe patient's physiological parameter values. The functions also includeadjusting scaling factors K_(M) based on the difference between actualcontrol parameter value A(t_(j)) and optimal control parameter valueR(t_(j)), the difference between actual control parameter value A(t_(i))and optimal control parameter value R(t_(i)), and the difference betweenactual self-care values C_(M)(t_(i)) and optimal self-care valuesO_(M)(t_(i)).

The operation of the second embodiment is shown in FIG. 14. FIG. 14 is aflow chart illustrating a preferred method of using the system of thesecond embodiment to predict an effect of patient self-care actions on adisease control parameter. In step 300, the optimal control parametervalues and optimal self-care values are entered in device 64 and storedin memory 74. The optimal control parameter values and optimal self-carevalues may be entered in device 64 either through keypad 68 or throughinput/output port 76.

In step 302, the patient or healthcare provider determines the patient'sphysiological parameter values. The physiological parameter values arethen entered in device 64 through keypad 68 and stored in memory 74.Following entry of the physiological parameter values, microprocessor 72determines scaling factors K_(M) from the indexes stored in memory 74,step 304. Scaling factors K_(M) are then stored in memory 74. In analternative method of determining and storing scaling factors K_(M) inmemory 74, scaling factors K_(M) are determined in a healthcare providercomputer, as previously described in the preferred embodiment. Scalingfactors K_(M) are then entered in device 64 through keypad 68 or port 76and stored in memory 74.

In step 306, the patient enters in microprocessor 72 actual diseasecontrol parameter A(t_(i)). To enter actual disease control parameterA(t_(i)), the patient places his or her finger on measurement device 40at time t_(i). Measurement device 40 produces a measurement of actualdisease control parameter A(t_(i)) which is stored in memory 74. In step308, the patient enters in microprocessor 72 patient self-care valuesS_(M)(t_(i)) using keypad 68. In step 310, microprocessor 72 executesthe simulation program stored in memory 74 to calculate future diseasecontrol parameter value X(t_(j)).

The simulation program of the second embodiment differs from thesimulation program of the preferred embodiment in that future diseasecontrol parameter value X(t_(j)) is calculated from actual diseasecontrol parameter A(t_(i)) rather than from a prior simulated diseasecontrol parameter value. In the second embodiment, future diseasecontrol parameter value X(t_(j)) is calculated according to theequation:${X( t_{j} )} = {{R( t_{j} )} + ( {{A( t_{i} )} - {R( t_{i} )} + {\sum\limits_{M}\quad{K_{M}( {{S_{M}( t_{i} )} - {O_{M}( t_{i} )}} )}}} }$

Thus, future disease control parameter value X(t_(j)) is determined fromoptimal control parameter value R(t_(i)), the difference between actualdisease control parameter A(t_(i)) and optimal control parameter valueR(t_(i)), and the set of differentials between patient self-care valuesS_(M)(t_(i)) and optimal self-care values O_(M)(t_(i)). Thedifferentials are multiplied by corresponding scaling factors K_(M).Future disease control parameter value X(t_(j)) is displayed to thepatient on display 70, step 312.

Once future disease control parameter value X(t_(j)) is displayed to thepatient, the patient uses the value to select appropriate actualself-care actions to perform at time t_(i). Alternatively, the patientmay perform several more simulations of future disease control parametervalue X(t_(j)) to decide appropriate self-care actions to perform attime t_(i). Once the patient has performed the self-care actions, thepatient enters in microprocessor 72 actual self-care values C_(M)(t_(i))indicating the self-care actions performed, step 314. The actualself-care values are then stored in memory 74.

The patient also enters in microprocessor 72 actual disease controlparameter A(t_(j)) measured at time t_(j). To enter actual diseasecontrol parameter A(t_(j)), the patient places his or her finger onmeasurement device 40 at time t_(j). Measurement device 40 produces ameasurement of actual disease control parameter A(t_(j)) which is storedin memory 74, step 316. In step 318, microprocessor 72 adjusts scalingfactors K_(M) based on the difference between actual control parametervalue A(t_(j)) and optimal control parameter value R(t_(j)), thedifference between actual control parameter value A(t_(i)) and optimalcontrol parameter value R(t_(i)), and the difference between actualself-care values C_(M)(t_(i)) and optimal self-care values O_(M)(t_(i)).In this manner, the scaling factors are customized to the individualpatient to enable the patient to run customized simulations. The newvalues of the scaling factors are stored in memory 74 and used bymicroprocessor 72 in subsequent simulations.

SUMMARY, RAMIFICATIONS, AND SCOPE

Although the above description contains many specificities, these shouldnot be construed as limitations on the scope of the invention but merelyas illustrations of some of the presently preferred embodiments. Manyother embodiments of the invention are possible. For example, thepreferred embodiment is described in relation to diabetes. However, thesystem and method of the invention may be used for simulating anydisease which has a measurable control parameter and which requirespatient self-care actions. Similarly, the self-care parameters,corresponding scaling factors, and physiological parameters describedare exemplary of just one possible embodiment. Many different self-careparameters, scaling factors, and physiological parameters may be used inalternative embodiments.

The preferred embodiment also presents a simulation system that includesa server, a healthcare provider computer, and patient multi-mediaprocessor communicating with the provider computer via a smart card.This configuration of system components is presently preferred for easeof setting, storing, and adjusting the model parameters and scalingfactors under the supervision of a healthcare provider. However, thoseskilled in the art will recognize that many other system configurationsare possible. For example, in one alternative embodiment, the system isconfigured as a single stand-alone computing device for executingsimulations.

In another embodiment, the smart card is eliminated from the simulationsystem. In this embodiment, the model parameter values and scalingfactors are transmitted directly to the server from the healthcareprovider computer. In a further embodiment, the provider computer isalso eliminated and the recording device is networked directly to theserver. In this embodiment, the server is programmed to set, store, andadjust the model parameters and scaling factors based on patient datareceived through the recording device and patient multi-media processor.

In yet another embodiment, the server is eliminated and the simulationprogram is run on the patient multi-media processor. In this embodiment,the recording device and multi-media processor may also be networkeddirectly to the provider computer, eliminating the need for a smartcard. Specific techniques for networking computers and recording devicesin these alternative system configurations are well known in the art.

Further, the first embodiment is described as a system for simulating adisease control parameter from simulated data and the second embodimentis described as a system for predicting a future value of a diseasecontrol parameter from actual patient data. These systems are presentedin separate embodiments for clarity of illustration and ease ofunderstanding. However, it is anticipated that both embodiments could becombined into a single simulation system for simulating disease controlparameter values from simulated data, actual-patient data, or acombination of simulated and actual data.

Therefore, the scope of the invention should be determined not by theexamples given but by the appended claims and their legal equivalents.

1. A diabetes care management system for managing blood glucose levelsassociated with diabetes, comprising: (a) a computing device,comprising: (i) a memory comprising one or more optimal blood glucosevalues, one or more insulin dose values of a patient, one or moremeasured blood glucose values, and one or more scaling factors forweighting the impact on a future blood glucose value and that arecustomizable to an individual patient to predict the effect on the bloodglucose of insulin dose actions performed by the individual patient;(ii) a microprocessor, in communication with said memory, programmed tocalculate a further value, said further value being based on saidinsulin dose values, said optimal blood glucose values, and said scalingfactors; (iii) a display configured to display information according tosaid further value; and (iv) a housing, wherein said memory and saidmicroprocessor are housed within said housing, thereby providing ahand-held, readily transportable computing device; and (b) an insulindelivery device for delivering insulin in response to informationassociated with said further value.
 2. The system of claim 1, whereinsaid memory further comprises one or more carbohydrate intake values,wherein said microprocessor is also programmed to calculate said furthervalue based on said one or more carbohydrate intake values.
 3. Thesystem of claim 1, wherein said display is configured for displayingmultiple values in graphical form.
 4. The system of claim 1, whereinsaid display of said further value enables the patient to considerappropriate insulin dose actions.
 5. The system of claim 1, furthercomprising an electronic data recording device configured for receivingprior blood glucose values.
 6. The system of claim 1, wherein saidmemory further comprises one or more optimal insulin dose values, andsaid further value is calculated based further upon said one or moreoptimal insulin dose values.
 7. The system of claim 1, wherein saidmemory is further programmed to determine said one or more scalingfactors from one or more physiological parameters including body mass,metabolism rate, fitness level or hepatic or peripheral insulinsensitivity, or combinations thereof.
 8. The system of claim 1, whereinsaid memory is further programmed to determine said one or more scalingfactors from insulin sensitivity.
 9. A diabetes care management systemfor managing blood glucose levels associated with diabetes, comprising:(a) a computing device, comprising: (i) a memory comprising one or moreoptimal blood glucose values, one or more carbohydrate intake values ofa patient, one or more measured blood glucose values, and one or morescaling factors for weighting the impact on a future blood glucose valueand that are customizable to an individual patient to predict the effecton the blood glucose of carbohydrate intake actions performed by theindividual patient; (ii) a microprocessor, in communication with saidmemory, programmed to calculate a further value, said further valuebeing based on said carbohydrate intake values, said optimal bloodglucose values, and said scaling factors; (iii) a display configured todisplay information according to said further value; and (iv) a housing,wherein said memory and said microprocessor are housed within saidhousing, thereby providing a hand-held, readily transportable computingdevice; and (b) an insulin delivery device for delivering insulin inresponse to information associated with said further value.
 10. Thesystem of claim 9, wherein said memory further comprises one or moreexercise values, wherein said microprocessor is also programmed tocalculate said further value based on said one or more exercise values.11. The system of claim 9, wherein said display of said further valueenables the patient to consider appropriate carbohydrate actions. 12.The system of claim 9, wherein said memory further comprises one or moreoptimal carbohydrate intake values, and said further value is calculatedbased further upon said optimal carbohydrate intake values.
 13. Thesystem of claim 9, wherein said memory is further programmed todetermine said one or more scaling factors from one or morephysiological parameters including body mass, metabolism rate, fitnesslevel or hepatic or peripheral insulin sensitivity, or combinationsthereof.
 14. The system of claim 9, wherein said memory is furtherprogrammed to determine said one or more scaling factors from body massor metabolism rate or both.
 15. The system of claim 14, wherein thememory further comprises one or more optimal self care values includingan optimal food exchange parameter value expressed in grams ofcarbohydrates, an optimal insulin dose parameter value expressed inunits of insulin, or an optimal exercise duration parameter valueexpressed in fifteen minute units of exercise, or combinations thereof.16. The system of claim 15, wherein said values are respectivelydetermined based on one gram of carbohydrate being expected to raise thepatient's blood glucose level by 4 mg/dL, one unit of insulin beingexpected to lower the patient's blood glucose level by 40 mg/dL, orfifteen minutes of exercise being expected to lower the patient's bloodglucose level by 5 mg/dL, or said combinations thereof.
 17. A diabetescare management system for managing blood glucose levels associated withdiabetes, comprising: (a) a computing device, comprising: (i) a memorycomprising one or more optimal blood glucose values, one or more insulindose, food intake or exercise values of a patient, or combinationsthereof, one or more measured blood glucose values, and one or morescaling factors for weighting the impact on a future blood glucose valueand that are customizable to an individual patient to predict the effecton the blood glucose of insulin dose, food intake or exercise actions,or combinations thereof, performed by the individual patient; (ii) amicroprocessor, in communication with said memory, programmed tocalculate a further value, said further value being based on saidinsulin dose, food intake or exercise values, or combinations thereof,and on said one or more optimal blood glucose values, and on said one ormore scaling factors; and (iii) a display configured to displayinformation according to said further value; and (iv) a housing, whereinsaid memory and said microprocessor are housed within said housing,thereby providing a hand-held, readily transportable computing device;and (b) an insulin delivery device for delivering insulin in response toinformation associated with said further value.
 18. The system of claim17, wherein said memory comprises one or more exercise values, whereinsaid microprocessor is programmed to calculate said further value basedon said one or more exercise values.
 19. The system of claim 17, furthercomprising an output port coupled to said processor for establishing acommunication link between said device and a healthcare providercomputer and for transmitting and receiving data therebetween.
 20. Thesystem of claim 17, further comprising an electronic data recordingdevice including a glucose measurement device for measuring the bloodglucose values, and from which the measured blood glucose values arestored in the memory.
 21. The system of claim 17, wherein said displayof said further value enables the patient to consider appropriateinsulin dose action including injecting units of insulin, food intakeactions including intake of grams of carbohydrates, or exercise actionsincluding minutes of exercise or durations of exercise in 15 minuteunits, or combinations thereof.
 22. The system of claim 17, furthercomprising an electronic data recording device configured for receivingprior blood glucose values.
 23. The system of claim 17, wherein saidmemory further comprises one or more optimal insulin dose, food intakeor exercise values, or combinations thereof, and said further value iscalculated based further upon said one or more optimal insulin dose,food intake or exercise values, or combinations thereof.
 24. The systemof claim 17, wherein said memory is further programmed to determine saidone or more scaling factors from one or more physiological parametersincluding body mass, metabolism rate, fitness level or hepatic orperipheral insulin sensitivity, or combinations thereof.
 25. The systemof claim 17, wherein the memory further comprises one or more optimalself care values including an optimal food exchange parameter valueexpressed in grams of carbohydrates, an optimal insulin dose parametervalue expressed in units of insulin, or an optimal exercise durationparameter value expressed in fifteen minute units of exercise, orcombinations thereof.